Per-Context Reverse Curricula for Sparse-Reward Reinforcement Learning
Sparse-reward reinforcement learning often fails because rollouts from the unassisted evaluation start rarely reach later task stages. Reverse curricula address this by starting some training rollouts from easier intermediate states, called scaffolds. The main choices are which intermediate starts to use (scaffold access) and how to distribute training across them (scaffold allocation). Most prior curricula pace removal on one shared schedule, which can fail when task instances, or contexts, learn at different rates. We introduce an online, learner-agnostic reset controller that gives every context its own curriculum. We use binary rollout success to decide whether to move a context's sta...
| Authors | Siddharth Aphale, Ayushman Singh |
|---|---|
| Primary category | cs.LG |
| Published | 29 July 2026 |
| Last updated at source | 11 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.